Plant Pathology Identification Using Digital Imaging

Abstract

This study presents an innovative system for identifying crop diseases using a deep learning approach based on the Mobile Net architecture. Designed for efficiency and lightweight performance, Mobile Net enables accurate disease detection from leaf images while being highly suitable for deployment on mobile devices. The system incorporates a user-friendly graphical interface and a dedicated mobile application, allowing farmers to upload leaf images directly from their smartphones and receive instant diagnoses along with recommended treatments. Trained on the Plant Village dataset, the model is optimized for identifying diseases affecting five major crops: corn, apple, sugarcane, wheat, and grapes. By surpassing the limitations of traditional methods such as K-means clustering and SVM, the proposed system offers higher accuracy, faster processing, and real-time accessibility. This solution aims to minimize crop losses, improve agricultural productivity, and empower farmers with a portable and practical tool for effective crop management.

Introduction

The Plant Pathology Identification Using Digital Imaging project develops an intelligent, lightweight, and scalable system for accurately detecting plant diseases from leaf images. By integrating machine learning and computer vision, the system assists farmers via a user-friendly web and mobile interface. Users upload leaf images which are preprocessed and classified using a TensorFlow Lite model, while an optional full TensorFlow model performs out-of-distribution detection to flag unfamiliar diseases. The system outputs disease identification along with treatment suggestions, helping to reduce crop losses, optimize pesticide use, and support sustainable farming.

The literature review highlights recent advances such as data augmentation, attention mechanisms, lightweight CNNs, transfer learning, and Vision Transformers that improve classification accuracy and real-time deployment.

The methodology includes data collection with expert labeling, web interface development for image upload, Flask backend processing, image preprocessing, model inference, out-of-distribution detection, result mapping, and responsive display of diagnosis and recommendations on both web and mobile platforms.

Results demonstrate high accuracy (>94%) in detecting grape leaf diseases using a MobileNetV2-based model, with a clean and intuitive UI that provides clear feedback, treatment advice, and handles unknown inputs gracefully. The architecture and user experience are consistent across web and mobile applications, promoting practical usability for growers.

Conclusion

This research demonstrates that a fine-tuned CNN-Attention architecture, trained on a custom plant-pathology image dataset, achieves state-of-the-art performance in real-time disease identification (F1-score: 0.96; accuracy: 95%; inference time: <50 ms/image). The proposed system overcomes key shortcomings of generic vision classifiers by: 1) High-Resolution Feature Preservation Processing full-resolution leaf images (e.g. 1024×1024) rather than aggressively down-sampling to 224×224, thereby retaining subtle lesion textures—such as early chlorotic spots—that coarse resizing erases. 2) Robust Out-of-Distribution (OOD) Detection Incorporating a lightweight auxiliary OOD detector that flags non-leaf inputs or unfamiliar backgrounds, reducing false-alarm rates from ~15% (generic models) to <3%. 3) Mobile-Ready Efficiency Utilizing depthwise-separable convolutions and channel-wise attention to deliver inference times under 50 ms on mid-range smartphones, enabling real-time field diagnosis without cloud dependency. However, the system’s performance is constrained by: • Dataset Bias: The custom dataset contains abundant late-stage disease samples but relatively few early-stage and rare-disease examples, which can reduce sensitivity for underrepresented classes. • Lighting & Background Variance: Accuracy dips (~5%) under extreme glare, heavy shadows, or cluttered field scenes, as revealed by controlled failure analyses. Key Improvements Over Generic Solutions

References

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Copyright

Copyright © 2025 Shiva Sharan Babburi, Deekshith Gade, Veera Mahidhar Mygapula, P Chandrasekhar Reddy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.